






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase								=		{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase								=		{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase								=		{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Dict:
							if isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('boolean value expected'				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=False				)     ->    Tuple:
							_lowercase							:   List[str] 							=   checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
							_lowercase							:   int 							=   checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
							_lowercase							:   List[str] 							=   checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

							if has_skip:
														_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.skip_connection.weight"""]
														_lowercase							:   int 							=   checkpoint[F"""{old_prefix}.skip_connection.bias"""]

							return new_checkpoint






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=None				)     ->    List[str]:
							_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3     ,     dim=0				)
							_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3     ,     dim=0				)

							_lowercase							:   str 							=   checkpoint[F"""{old_prefix}.norm.weight"""]
							_lowercase							:   Tuple 							=   checkpoint[F"""{old_prefix}.norm.bias"""]

							_lowercase							:   int 							=   weight_q.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[Any] 							=   bias_q.squeeze(-1				).squeeze(-1				)
							_lowercase							:   int 							=   weight_k.squeeze(-1				).squeeze(-1				)
							_lowercase							:   Any 							=   bias_k.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[str] 							=   weight_v.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[str] 							=   bias_v.squeeze(-1				).squeeze(-1				)

							_lowercase							:   Tuple 							=   (
							    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1				).squeeze(-1				)
							)
							_lowercase							:   Dict 							=   checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1				).squeeze(-1				)

							return new_checkpoint






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   str 							=   torch.load(SCREAMING_SNAKE_CASE     ,     map_location='cpu'				)
							_lowercase							:   str 							=   {}

							_lowercase							:   str 							=   checkpoint['time_embed.0.weight']
							_lowercase							:   Tuple 							=   checkpoint['time_embed.0.bias']
							_lowercase							:   Optional[Any] 							=   checkpoint['time_embed.2.weight']
							_lowercase							:   int 							=   checkpoint['time_embed.2.bias']

							if unet_config["num_class_embeds"] is not None:
														_lowercase							:   Dict 							=   checkpoint['label_emb.weight']

							_lowercase							:   Tuple 							=   checkpoint['input_blocks.0.0.weight']
							_lowercase							:   Optional[Any] 							=   checkpoint['input_blocks.0.0.bias']

							_lowercase							:   Union[str, Any] 							=   unet_config['down_block_types']
							_lowercase							:   Tuple 							=   unet_config['layers_per_block']
							_lowercase							:   Any 							=   unet_config['attention_head_dim']
							_lowercase							:   List[Any] 							=   unet_config['block_out_channels']
							_lowercase							:   Optional[Any] 							=   1
							_lowercase							:   List[str] 							=   channels_list[0]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE				):
														_lowercase							:   Tuple 							=   channels_list[i]
														_lowercase							:   Tuple 							=   current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE				):
																												_lowercase							:   str 							=   F"""down_blocks.{i}.resnets.{j}"""
																												_lowercase							:   Dict 							=   F"""input_blocks.{current_layer}.0"""
																												_lowercase							:   int 							=   True if j == 0 and downsample_block_has_skip else False
																												_lowercase							:   Optional[int] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE				):
																												_lowercase							:   Tuple 							=   F"""down_blocks.{i}.resnets.{j}"""
																												_lowercase							:   Union[str, Any] 							=   F"""input_blocks.{current_layer}.0"""
																												_lowercase							:   List[Any] 							=   True if j == 0 and downsample_block_has_skip else False
																												_lowercase							:   Union[str, Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												_lowercase							:   Dict 							=   F"""down_blocks.{i}.attentions.{j}"""
																												_lowercase							:   int 							=   F"""input_blocks.{current_layer}.1"""
																												_lowercase							:   Any 							=   convert_attention(
																												    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												current_layer += 1

														if i != len(SCREAMING_SNAKE_CASE				) - 1:
																					_lowercase							:   int 							=   F"""down_blocks.{i}.downsamplers.0"""
																					_lowercase							:   List[Any] 							=   F"""input_blocks.{current_layer}.0"""
																					_lowercase							:   Tuple 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					current_layer += 1

														_lowercase							:   List[Any] 							=   current_channels

							# hardcoded the mid-block for now
							_lowercase							:   List[Any] 							=   'mid_block.resnets.0'
							_lowercase							:   Union[str, Any] 							=   'middle_block.0'
							_lowercase							:   Optional[int] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   Tuple 							=   'mid_block.attentions.0'
							_lowercase							:   Any 							=   'middle_block.1'
							_lowercase							:   str 							=   convert_attention(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   Optional[Any] 							=   'mid_block.resnets.1'
							_lowercase							:   Optional[Any] 							=   'middle_block.2'
							_lowercase							:   List[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   Optional[int] 							=   0
							_lowercase							:   List[str] 							=   unet_config['up_block_types']

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												_lowercase							:   Tuple 							=   F"""up_blocks.{i}.resnets.{j}"""
																												_lowercase							:   int 							=   F"""output_blocks.{current_layer}.0"""
																												_lowercase							:   str 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE				) - 1:
																												_lowercase							:   Dict 							=   F"""up_blocks.{i}.upsamplers.0"""
																												_lowercase							:   Optional[Any] 							=   F"""output_blocks.{current_layer-1}.1"""
																												_lowercase							:   Dict 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												_lowercase							:   Tuple 							=   F"""up_blocks.{i}.resnets.{j}"""
																												_lowercase							:   List[str] 							=   F"""output_blocks.{current_layer}.0"""
																												_lowercase							:   Optional[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												_lowercase							:   str 							=   F"""up_blocks.{i}.attentions.{j}"""
																												_lowercase							:   Dict 							=   F"""output_blocks.{current_layer}.1"""
																												_lowercase							:   Optional[Any] 							=   convert_attention(
																												    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE				) - 1:
																												_lowercase							:   int 							=   F"""up_blocks.{i}.upsamplers.0"""
																												_lowercase							:   str 							=   F"""output_blocks.{current_layer-1}.2"""
																												_lowercase							:   List[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   List[Any] 							=   checkpoint['out.0.weight']
							_lowercase							:   Optional[Any] 							=   checkpoint['out.0.bias']
							_lowercase							:   Dict 							=   checkpoint['out.2.weight']
							_lowercase							:   Dict 							=   checkpoint['out.2.bias']

							return new_checkpoint


if __name__ == "__main__":
							UpperCamelCase								=		argparse.ArgumentParser()

							parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
							parser.add_argument(
							    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
							)
							parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

							UpperCamelCase								=		parser.parse_args()
							UpperCamelCase								=		strabool(args.class_cond)

							UpperCamelCase								=		os.path.basename(args.unet_path)
							print(f'''Checkpoint: {ckpt_name}''')

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														UpperCamelCase								=		IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														UpperCamelCase								=		LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														UpperCamelCase								=		TEST_UNET_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							if not args.class_cond:
														UpperCamelCase								=		None

							UpperCamelCase								=		con_pt_to_diffuser(args.unet_path, unet_config)

							UpperCamelCase								=		UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														UpperCamelCase								=		CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														UpperCamelCase								=		CT_IMAGENET_64_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														UpperCamelCase								=		CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							UpperCamelCase								=		CMStochasticIterativeScheduler(**scheduler_config)

							UpperCamelCase								=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)





'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
